A robust gene regulatory network inference method base on Kalman filter and linear regression

PloS One
Jamshid Pirgazi, Ali Reza Khanteymoori

Abstract

The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology. The main challenge in gene expression data is the high number of genes and low number of samples; also the data are often impregnated with noise. In this paper, in dealing with the noisy data, Kalman filter based method that has the ability to use prior knowledge on learning the network was used. In the proposed method namely (KFLR), in the first phase by using mutual information, the noisy regulations with low correlations were removed. The proposed method utilized a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene within a hybrid framework of Bayesian model averaging and linear regression methods. In order to show the efficiency, the proposed method was compared with several well know methods. The results of the evaluation indicate that the inference accuracy was improved by the proposed method which also demonstrated better regulatory relations with the noisy data.

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Citations

Jul 11, 2019·Journal of Bioinformatics and Computational Biology·Jamshid PirgaziMaryam Jalilkhani
Apr 30, 2019·Frontiers in Genetics·Abhijeet R SonawaneAmitabh Sharma
Feb 13, 2021·Bioinformatics·Yuelei ZhangXiaoping Liu
Mar 5, 2021·Journal of Bioinformatics and Computational Biology·Jamshid PirgaziAlireza Khanteymoori
Mar 17, 2020·Computers in Biology and Medicine·Turki Turki, Y-H Taguchi

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Software Mentioned

SIRENE
TIGRESS
MIBNI
CMI2NI
GENIE3
java
R
minet
GENIRF
GENIES

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